Estimating the spend capacity of consumer households

ABSTRACT

The spend capacity of a consumer typically increases as the number of consumers in the household increases, since the consumer can draw on the spending power of other consumers in the household. The size of wallet of the household is thus a better indicator of the consumer&#39;s spend capacity than an individual size of wallet. All consumers in a given household can be aggregated based on, for example, their address of record. Duplicate tradelines within each household are removed from consideration in a size of wallet estimate. A spend capacity is then estimated for each tradeline using calculations derived from a consumer behavior model. The spend capacities for all tradelines in the household are combined to determine a household size of wallet. Each consumer in the household is then tagged with the household size of wallet, rather than their individual size of wallet.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of claims priority to and thebenefit of, U.S. patent application Ser. No. 13/432,890, filed Mar. 28,2012 and entitled “ESTIMATING THE SPEND CAPACITY OF CONSUMERHOUSEHOLDS.” The '890 application is a continuation of, claims priorityto and the benefit of, U.S. Pat. No. 8,204,774 issued Jun. 19, 2012 (akaU.S. patent application Ser. No. 11/611,699, filed Dec. 15, 2006) andentitled “ESTIMATING THE SPEND CAPACITY OF CONSUMER HOUSEHOLDS.” The'774 patent is a continuation-in-part of, claims priority to and thebenefit of, U.S. Pat. No. 7,788,147 issued Aug. 31, 2010 (aka U.S.patent application Ser. No. 10/978,298, filed Oct. 29, 2004) andentitled “METHOD AND APPARATUS FOR ESTIMATING THE SPEND CAPACITY OFCONSUMERS.” All the aforementioned patents and applications areincorporated by reference herein in their entirety.

BACKGROUND OF THE INVENTION

Field

This disclosure generally relates to financial data processing, and inparticular it relates to customer profiling and consumer behavioranalysis.

Background Art

It is axiomatic that consumers will tend to spend more when they havegreater purchasing power. The capability to accurately estimate aconsumer's spend capacity could therefore allow a financial institution(such as a credit company, lender or any consumer services company) tobetter target potential prospects and identify any opportunities toincrease consumer transaction volumes, without an undue increase in therisk of defaults. Attracting additional consumer spending in thismanner, in turn, would increase such financial institution's revenues,primarily in the form of an increase in transaction fees and interestpayments received. Consequently, accurate estimation of purchasing poweris of paramount interest to many financial institutions and otherconsumer services companies.

The purchasing power of an individual consumer is often related to thetotal purchasing power of the consumer's household. However,understanding spending at a household level has been a challenge forfinancial institutions, because it is very difficult to group consumersby household. This is especially problematic when other individuals inthe household do not maintain tradelines with the financial institution.Accordingly, there is a need for a method and apparatus for identifyingmembers of a household of a consumer and determining the size of walletof the entire household.

BRIEF SUMMARY

The spend capacity of a consumer typically increases as the number ofconsumers in the household increases. This occurs because an individualconsumer can draw on the spending power of other consumers in thehousehold. Identifying these consumers and determining the size ofwallet of their households is beneficial to a financial institution, asit allows the financial institution to better target the consumerswithout increasing the risk of default by the consumers. In an exemplarymethod, all individuals in a given household are aggregated based on,for example, their address of record. Duplicate tradelines within eachhousehold are removed from consideration in a size of wallet estimate. Aspend capacity is then estimated for each tradeline using calculationsderived from a consumer behavior model. The spend capacities for alltradelines in the household are combined to determine a household sizeof wallet. Each consumer in the household is then tagged with thehousehold size of wallet, rather than their individual size of wallet.

When consumer spending levels are reliably identified in this manner,consumers may be categorized to more effectively manage the customerrelationship and increase the profitability therefrom. For instance, afinancial institution can better identify customers who would mostbenefit from an offer for a new product or service or who would be mostlikely to increase their transaction volumes. High spending householdscan be targeted with the institution's best product offers andincentives, which encourages spending by members of that household usingthe account held at the financial institution.

Further embodiments, features, and advantages of the present invention,as well as the structure and operation of the various embodiments of thepresent invention, are described in detail below with reference to theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

The accompanying drawings, which are incorporated herein and form a partof the specification, illustrate the present invention and, togetherwith the description, further serve to explain the principles of theinvention and to enable a person skilled in the pertinent art to makeand use the invention.

FIGS. 1A and 1B are block diagrams of an exemplary financial dataexchange network over which the processes of the present disclosure maybe performed;

FIG. 2 is a flowchart of an exemplary consumer modeling processperformed by the financial server of FIG. 1;

FIG. 3 is a diagram of exemplary categories of consumers examined duringthe process of FIG. 2;

FIG. 4 is a diagram of exemplary subcategories of consumers modeledduring the process of FIG. 2;

FIG. 5 is a diagram of financial data used for model generation andvalidation according to the process of FIG. 2;

FIG. 6 is a flowchart of an exemplary process for estimating the spendability of a consumer, performed by the financial server of FIG. 1;

FIG. 7-10 are exemplary timelines showing the rolling time periods forwhich individual customer data is examined during the process of FIG. 6;and

FIG. 11-19 are tables showing exemplary results and outputs of theprocess of FIG. 6 against a sample consumer population.

FIG. 20 is a flowchart of an exemplary method for determining ahousehold size of wallet.

FIG. 21 is a chart identifying various example household types.

FIG. 22 is a chart illustrating average sizes of wallet by householdtype.

FIG. 23 is a chart illustrating spend opportunity based on an exemplaryshare of wallet distribution.

The present invention will be described with reference to theaccompanying drawings. The drawing in which an element first appears istypically indicated by the leftmost digit(s) in the correspondingreference number.

DETAILED DESCRIPTION I. Overview

While specific configurations and arrangements are discussed, it shouldbe understood that this is done for illustrative purposes only. A personskilled in the pertinent art will recognize that other configurationsand arrangements can be used without departing from the spirit and scopeof the present invention. It will be apparent to a person skilled in thepertinent art that this invention can also be employed in a variety ofother applications.

The terms “user,” “end user,” “consumer,” “customer,” “participant,”and/or the plural form of these terms are used interchangeablythroughout herein to refer to those persons or entities capable ofaccessing, using, being affected by and/or benefiting from the tool thatthe present invention provides for determining a household size ofwallet.

Furthermore, the terms “business” or “merchant” may be usedinterchangeably with each other and shall mean any person, entity,distributor system, software and/or hardware that is a provider, brokerand/or any other entity in the distribution chain of goods or services.For example, a merchant may be a grocery store, a retail store, a travelagency, a service provider, an on-line merchant or the like.

1. Transaction Accounts and Instrument

A “transaction account” as used herein refers to an account associatedwith an open account or a closed account system (as described below).The transaction account may exist in a physical or non-physicalembodiment. For example, a transaction account may be distributed innon-physical embodiments such as an account number, frequent-flyeraccount, telephone calling account or the like. Furthermore, a physicalembodiment of a transaction account may be distributed as a financialinstrument.

A financial transaction instrument may be traditional plastictransaction cards, titanium-containing, or other metal-containing,transaction cards, clear and/or translucent transaction cards, foldableor otherwise unconventionally-sized transaction cards, radio-frequencyenabled transaction cards, or other types of transaction cards, such ascredit, charge, debit, pre-paid or stored-value cards, or any other likefinancial transaction instrument. A financial transaction instrument mayalso have electronic functionality provided by a network of electroniccircuitry that is printed or otherwise incorporated onto or within thetransaction instrument (and typically referred to as a “smart card”), orbe a fob having a transponder and an RFID reader.

2. Use of Transaction Accounts

With regard to use of a transaction account, users may communicate withmerchants in person (e.g., at the box office), telephonically, orelectronically (e.g., from a user computer via the Internet). During theinteraction, the merchant may offer goods and/or services to the user.The merchant may also offer the user the option of paying for the goodsand/or services using any number of available transaction accounts.Furthermore, the transaction accounts may be used by the merchant as aform of identification of the user. The merchant may have a computingunit implemented in the form of a computer-server, although otherimplementations are possible.

In general, transaction accounts may be used for transactions betweenthe user and merchant through any suitable communication means, such as,for example, a telephone network, intranet, the global, public Internet,a point of interaction device (e.g., a point of sale (POS) device,personal digital assistant (FDA), mobile telephone, kiosk, etc.), onlinecommunications, off-line communications, wireless communications, and/orthe like.

A transaction account has a basic user, who is the primary userassociated with the account. A transaction account may also have asupplemental user who is given access to the account by the basic user.The supplemental user may possess a duplicate of the transactioninstrument associated with the account.

3. Account and Merchant Numbers

An “account,” “account number” or “account code”, as used herein, mayinclude any device, code, number, letter, symbol, digital certificate,smart chip, digital signal, analog signal, biometric or otheridentifier/indicia suitably configured to allow a consumer to access,interact with or communicate with a financial transaction system. Theaccount number may optionally be located on or associated with anyfinancial transaction instrument (e.g., rewards, charge, credit, debit,prepaid, telephone, embossed, smart, magnetic stripe, bar code,transponder or radio frequency card).

Persons skilled in the relevant arts will understand the breadth of theterms used herein and that the exemplary descriptions provided are notintended to be limiting of the generally understood meanings attributedto the foregoing terms.

It is noted that references in the specification to “one embodiment”,“an embodiment”, “an example embodiment”, etc., indicate that theembodiment described may include a particular feature, structure, orcharacteristic, but every embodiment may not necessarily include theparticular feature, structure, or characteristic. Moreover, such phrasesare not necessarily referring to the same embodiment. Further, when aparticular feature, structure, or characteristic is described inconnection with an embodiment, it would be within the knowledge of oneskilled in the art to effect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

While specific configurations and arrangements are discussed, it shouldbe understood that this is done for illustrative purposes only. A personskilled in the pertinent art will recognize that other configurationsand arrangements can be used without departing from the spirit and scopeof the present invention. It will be apparent to a person skilled in thepertinent art that this invention can also be employed in a variety ofother applications.

As used herein, the following terms shall have the following meanings. Atrade or tradeline refers to a credit or charge vehicle issued to anindividual customer by a credit grantor. Types of tradelines include,for example and without limitation, bank loans, credit card accounts,retail cards, personal lines of credit and car loans/leases. Forpurposes here, use of the term credit card shall be construed to includecharge cards except as specifically noted. Tradeline data describes thecustomer's account status and activity, including, for example, names ofcompanies where the customer has accounts, dates such accounts wereopened, credit limits, types of accounts, balances over a period of timeand summary payment histories. Tradeline data is generally available forthe vast majority of actual consumers. Tradeline data, however, does notinclude individual transaction data, which is largely unavailablebecause of consumer privacy protections. Tradeline data may be used todetermine both individual and aggregated consumer spending patterns, asdescribed herein.

Consumer panel data measures consumer spending patterns from informationthat is provided by, typically, millions of participating consumerpanelists. Such consumer panel data is available through variousconsumer research companies, such as comScore Networks, Inc. of Reston,Va. Consumer panel data may typically include individual consumerinformation such as credit risk scores, credit card application data,credit card purchase transaction data, credit card statement views,tradeline types, balances, credit limits, purchases, balance transfers,cash advances, payments made, finance charges, annual percentage ratesand fees charged. Such individual information from consumer panel data,however, is limited to those consumers who have participated in theconsumer panel, and so such detailed data may not be available for allconsumers.

Although the present invention is described as relating to individualconsumers, one of skill in the pertinent art(s) will recognize that itcan also apply to small businesses and organizations without departingfrom the spirit and scope of the present invention.

II. Consumer Panel Data and Model Development/Validation

Technology advances have made it possible to store, manipulate and modellarge amounts of time series data with minimal expenditure on equipment.As will now be described, a financial institution may leverage thesetechnological advances in conjunction with the types of consumer datapresently available in the marketplace to more readily estimate thespend capacity of potential and actual customers. A reliable capabilityto assess the size of a consumer's wallet is introduced in whichaggregate time series and raw tradeline data are used to model consumerbehavior and attributes, and identify categories of consumers based onaggregate behavior. The use of raw trade-line time series data, andmodeled consumer behavior attributes, including but not limited to,consumer panel data and internal consumer data, allows actual consumerspend behavior to be derived from point in time balance information.

In addition, the advent of consumer panel data provided through internetchannels provides continuous access to actual consumer spend informationfor model validation and refinement. Industry data, including consumerpanel information having consumer statement and individual transactiondata, may be used as inputs to the model and for subsequent verificationand validation of its accuracy. The model is developed and refined usingactual consumer information with the goals of improving the customerexperience and increasing billings growth by identifying and leveragingincreased consumer spend opportunities.

A credit provider or other financial institution may also make use ofinternal proprietary customer data retrieved from its stored internalfinancial records. Such internal data provides access to even moreactual customer spending information, and may be used in thedevelopment, refinement and validation of aggregated consumer spendingmodels, as well as verification of the models' applicability to existingindividual customers on an ongoing basis.

While there has long been market place interest in understanding spendto align offers with consumers and assign credit line size, the holisticapproach of using a size of wallet calculation across customers'lifecycles (that is, acquisitions through collections) has notpreviously been provided. The various data sources outlined aboveprovide the opportunity for unique model logic development anddeployment, and as described in more detail in the following, variouscategories of consumers may be readily identified from aggregate andindividual data. In certain embodiments of the processes disclosedherein, the models may be used to identify specific types of consumers,nominally labeled ‘transactors’ and ‘revolvers,’ based on aggregatespending behavior, and to then identify individual customers andprospects that fall into one of these categories. Consumers falling intothese categories may then be offered commensurate purchasing incentivesbased on the model's estimate of consumer spending ability.

Referring now to FIGS. 1A, 1B, and 2-19, wherein similar components ofthe present disclosure are referenced in like manner, variousembodiments of a method and system for estimating the purchasing abilityof consumers will now be described in detail.

Turning now to FIG. 1A, there is depicted an exemplary computer network100 over which the transmission of the various types of consumer data asdescribed herein may be accomplished, using any of a variety ofavailable computing components for processing such data in the mannersdescribed below. Such components may include an institution computer102, which may be a computer, workstation or server, such as thosecommonly manufactured by IBM, and operated by a financial institution orthe like. The institution computer 102, in turn, has appropriateinternal hardware, software, processing, memory and networkcommunication components that enables it to perform the functionsdescribed here, including storing both internally and externallyobtained individual or aggregate consumer data in appropriate memory andprocessing the same according to the processes described herein usingprogramming instructions provided in any of a variety of useful machinelanguages. Institution computer 102 is described in further detail withrespect to FIG. 1B.

As shown in FIG. 1B, the institution computer 102 includes one or moreprocessors, such as processor 114. The processor 114 is connected to acommunication infrastructure 116 (e.g., a communications bus, cross-overbar, or network). Various software embodiments are described in terms ofthis exemplary computer system. After reading this description, it willbecome apparent to a person skilled in the relevant art(s) how toimplement the invention using other computer systems and/orarchitectures.

Institution computer 102 can include a display interface 112 thatforwards graphics, text, and other data from the communicationinfrastructure 116 (or from a frame buffer not shown) for display on thedisplay unit 140.

Institution computer 102 also includes a main memory 118, preferablyrandom access memory (RAM), and may also include a secondary memory 120.The secondary memory 120 may include, for example, a hard disk drive 122and/or a removable storage drive 124, representing a floppy disk drive,a magnetic tape drive, an optical disk drive, etc. The removable storagedrive 124 reads from and/or writes to a removable storage unit 128 in awell known manner. Removable storage unit 128 represents a floppy disk,magnetic tape, optical disk, etc. which is read by and written to byremovable storage drive 124. As will be appreciated, the removablestorage unit 128 includes a computer usable storage medium having storedtherein computer software and/or data.

In alternative embodiments, secondary memory 120 may include othersimilar devices for allowing computer programs or other instructions tobe loaded into institution computer 102. Such devices may include, forexample, a removable storage unit 128 and an interface 130. Examples ofsuch may include a program cartridge and cartridge interface (such asthat found in video game devices), a removable memory chip (such as anerasable programmable read only memory (EPROM), or programmable readonly memory (PROM) and associated socket, and other removable storageunits 128 and interfaces 130, which allow software and data to betransferred from the removable storage unit 128 to institution computer102.

Institution computer 102 may also include a communications interface134. Communications interface 134 allows software and data to betransferred between institution computer 102 and external devices.Examples of communications interface 134 may include a modem, a networkinterface (such as an Ethernet card), a communications port, a PersonalComputer Memory Card International Association (PCMCIA) slot and card,etc. Software and data transferred via communications interface 134 arein the form of signals 138 which may be electronic, electromagnetic,optical or other signals capable of being received by communicationsinterface 134. These signals 138 are provided to communicationsinterface 134 via a communications path (e.g., channel) 136. Thischannel 136 carries signals 138 and may be implemented using wire orcable, fiber optics, a telephone line, a cellular link, a radiofrequency (RF) link and other communications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media such as removablestorage drive 124 and a hard disk installed in hard disk drive 122.These computer program products provide software to institution computer102. The invention is directed to such computer program products.

Computer programs (also referred to as computer control logic) arestored in main memory 118 and/or secondary memory 120. Computer programsmay also be received via communications interface 134. Such computerprograms, when executed, enable the institution computer 102 to performthe features of the present invention, as discussed herein, inparticular, the computer programs, when executed, enable the processor114 to perform the features of the present invention. Accordingly, suchcomputer programs represent controllers of the institution computer 102.

In an embodiment where the invention is implemented using software, thesoftware may be stored in a computer program product and loaded intoinstitution computer 102 using removable storage drive 124, hard drive122 or communications interface 134. The control logic (software), whenexecuted by the processor 114, causes the processor 114 to perform thefunctions of the invention as described herein.

In another embodiment, the invention is implemented primarily inhardware using, for example, hardware components such as applicationspecific integrated circuits (ASICs). Implementation of the hardwarestate machine so as to perform the functions described herein will beapparent to persons skilled in the relevant art(s).

In yet another embodiment, the invention is implemented using acombination of both hardware and software.

The institution computer 102 may in turn be in operative communicationwith any number of other internal or external computing devices,including for example components 104, 106, 108, and 110, which may becomputers or servers of similar or compatible functional configuration.These components 104-110 may gather and provide aggregated andindividual consumer data, as described herein, and transmit the same forprocessing and analysis by the institution computer 102. Such datatransmissions may occur for example over the Internet or by any otherknown communications infrastructure, such as a local area network, awide area network, a wireless network, a fiber-optic network, or anycombination or interconnection of the same. Such communications may alsobe transmitted in an encrypted or otherwise secure format, in any of awide variety of known manners.

Each of the components 104-110 may be operated by either common orindependent entities. In one exemplary embodiment, which is not to belimiting to the scope of the present disclosure, one or more suchcomponents 104-110 may be operated by a provider of aggregate andindividual consumer tradeline data, an example of which includesservices provided by Experian Information Solutions, Inc. of Costa Mesa,Calif. (“Experian”). Tradeline level data preferably includes up to 24months or more of balance history and credit attributes captured at thetradeline level, including information about accounts as reported byvarious credit grantors, which in turn may be used to derive a broadview of actual aggregated consumer behavioral spending patterns.

Alternatively, or in addition thereto, one or more of the components104-110 may likewise be operated by a provider of individual andaggregate consumer panel data, such as commonly provided by comScoreNetworks, Inc. of Reston, Va. (“comScore”). Consumer panel data providesmore detailed and specific consumer spending information regardingmillions of consumer panel participants, who provide actual spend datato collectors of such data in exchange for various inducements. The datacollected may include any one or more of credit risk scores, onlinecredit card application data, online credit card purchase transactiondata, online credit card statement views, credit trade type and creditissuer, credit issuer code, portfolio level statistics, credit bureaureports, demographic data, account balances, credit limits, purchases,balance transfers, cash advances, payment amounts, finance charges,annual percentage interest rates on accounts, and fees charged, all atan individual level for each of the participating panelists. In variousembodiments, this type of data is used for model development, refinementand verification. This type of data is further advantageous overtradeline level data alone for such purposes, since such detailedinformation is not provided at the tradeline level. While such detailedconsumer panel data can be used alone to generate a model, it may not bewholly accurate with respect to the remaining marketplace of consumersat large without further refinement. Consumer panel data may also beused to generate aggregate consumer data for model derivation anddevelopment.

Additionally, another source of inputs to the model may be internalspend and payment history of the institution's own customers. From suchinternal data, detailed information at the level of specificity as theconsumer panel data may be obtained and used for model development,refinement and validation, including the categorization of consumersbased on identified transactor and revolver behaviors.

Turning now to FIG. 2, there is depicted a flowchart of an exemplaryprocess 200 for modeling aggregate consumer behavior in accordance withthe present disclosure. The process 200 commences at step 202 whereinindividual and aggregate consumer data, including time-series tradelinedata, consumer panel data and internal customer financial data, isobtained from any of the data sources described previously as inputs forconsumer behavior models. In certain embodiments, the individual andaggregate consumer data may be provided in a variety of different dataformats or structures and consolidated to a single useful format orstructure for processing.

Next, at step 204, the individual and aggregate consumer data isanalyzed to determine consumer spending behavior patterns. One ofordinary skill in the art will readily appreciate that the models mayinclude formulas that mathematically describe the spending behavior ofconsumers. The particular formulas derived will therefore highly dependon the values resulting from customer data used for derivation, as willbe readily appreciated. However, by way of example only and based on thedata provided, consumer behavior may be modeled by first dividingconsumers into categories that may be based on account balance levels,demographic profiles, household income levels or any other desiredcategories. For each of these categories in turn, historical accountbalance and transaction information for each of the consumers may betracked over a previous period of time, such as one to two years.Algorithms may then be employed to determine formulaic descriptions ofthe distribution of aggregate consumer information over the course ofthat period of time for the population of consumers examined, using anyof a variety of known mathematical techniques. These formulas in turnmay be used to derive or generate one or more models (step 206) for eachof the categories of consumers using any of a variety of available trendanalysis algorithms. The models may yield the following types ofaggregated consumer information for each category: average balances,maximum balances, standard deviation of balances, percentage of balancesthat change by a threshold amount, and the like.

Finally, at step 208, the derived models may be validated andperiodically refined using internal customer data and consumer paneldata from sources such as comScore. In various embodiments, the modelmay be validated and refined over time based on additional aggregatedand individual consumer data as it is continuously received by aninstitution computer 102 over the network 100. Actual customertransaction level information and detailed consumer information paneldata may be calculated and used to compare actual consumer spend amountsfor individual consumers (defined for each month as the differencebetween the sum of debits to the account and any balance transfers intothe account) and the spend levels estimated for such consumers using theprocess 200 above. If a large error is demonstrated between actual andestimated amounts, the models and the formulas used may be manually orautomatically refined so that the error is reduced. This allows for aflexible model that has the capability to adapt to actual aggregatedspending behavior as it fluctuates over time.

As shown in the diagram 300 of FIG. 3, a population of consumers forwhich individual and/or aggregated data has been provided may be dividedfirst into two general categories for analysis, for example, those thatare current on their credit accounts (representing 1.72 millionconsumers in the exemplary data sample size of 1.78 million consumers)and those that are delinquent (representing 0.06 million of suchconsumers). In one embodiment, delinquent consumers may be discardedfrom the populations being modeled.

In further embodiments, the population of current consumers is thensubdivided into a plurality of further categories based on the amount ofbalance information available and the balance activity of such availabledata. In the example shown in the diagram 300, the amount of balanceinformation available is represented by string of ‘+’ ‘0’ and ‘?’characters. Each character represents one month of available data, withthe rightmost character representing the most current months and theleftmost character representing the earliest month for which data isavailable. In the example provided in FIG. 3, a string of six charactersis provided, representing the six most recent months of data for eachcategory. The “+” character represents a month in which a credit accountbalance of the consumer has increased. The “0” character may representmonths where the account balance is zero. The “?” character representsmonths for which balance data is unavailable. Also provided the diagramis number of consumers fallen to each category and the percentage of theconsumer population they represent in that sample.

In further embodiments, only certain categories of consumers may beselected for modeling behavior. The selection may be based on thosecategories that demonstrate increased spend on their credit balancesover time. However, it should be readily appreciated that othercategories can be used. FIG. 3 shows the example of two categories ofselected consumers for modeling in bold. These groups show theavailability of at least the three most recent months of balance dataand that the balances increased in each of those months.

Turning now to FIG. 4, therein is depicted an exemplary diagram 400showing sub-categorization of the two categories of FIG. 3 in bold thatare selected for modeling. In the embodiment shown, the sub-categoriesmay include: consumers having a most recent credit balance less than$400; consumers having a most recent credit balance between $400 and$1600; consumers having a most recent credit balance between $1600 and$5000; consumers whose most recent credit balance is less than thebalance of, for example, three months ago; consumers whose maximumcredit balance increase over, for example, the last twelve monthsdivided by the second highest maximum balance increase over the sameperiod is less than 2; and consumers whose maximum credit balanceincrease over the last twelve months divided by the second highestmaximum balance increase is greater than 2. It should be readilyappreciated that other subcategories can be used. Each of thesesub-categories is defined by their last month balance level. The numberof consumers from the sample population (in millions) and the percentageof the population for each category are also shown in FIG. 4.

There may be a certain balance threshold established, wherein if aconsumer's account balance is too high, their behavior may not bemodeled, since such consumers are less likely to have sufficientspending ability. Alternatively, or in addition thereto, consumershaving balances above such threshold may be sub-categorized yet again,rather than completely discarded from the sample. In the example shownin FIG. 4, the threshold value may be $5000, and only those havingparticular historical balance activity may be selected, i.e. thoseconsumers whose present balance is less than their balance three monthsearlier, or whose maximum balance increase in the examined period meetscertain parameters. Other threshold values may also be used and may bedependent on the individual and aggregated consumer data provided.

As described in the foregoing, the models generated in the process 200may be derived, validated and refined using tradeline and consumer paneldata. An example of tradeline data 500 from Experian and consumer paneldata 502 from comScore are represented in FIG. 5. Each row of the data500, 502 represents the record of one consumer and thousands of suchrecords may be provided at a time. The statement 500 shows thepoint-in-time balance of consumers accounts for three successive months(Balance 1, Balance 2 and Balance 3). The data 502 shows each consumer'spurchase volume, last payment amount, previous balance amount andcurrent balance. Such information may be obtained, for example, by pagescraping the data (in any of a variety of known manners usingappropriate application programming interfaces) from an Internet website or network address at which the data 502 is displayed. Furthermore,the data 500 and 502 may be matched by consumer identity and combined byone of the data providers or another third party independent of thefinancial institution. Validation of the models using the combined data500 and 502 may then be performed, and such validation may beindependent of consumer identity.

Turning now to FIG. 6, therein is depicted an exemplary process 600 forestimating the size of an individual consumer's spending wallet. Uponcompletion of the modeling of the consumer categories above, the process600 commences with the selection of individual consumers or prospects tobe examined (step 602). An appropriate model derived during the process200 will then be applied to the presently available consumer tradelineinformation in the following manner to determine, based on the resultsof application of the derived models, an estimate of a consumer's sizeof wallet. Each consumer of interest may be selected based on theirfalling into one of the categories selected for modeling describedabove, or may be selected using any of a variety of criteria.

The process 600 continues to step 604 where, for a selected consumer, apaydown percentage over a previous period of time is estimated for eachof the consumer's credit accounts. In one embodiment, the paydownpercentage is estimated over the previous three-month period of timebased on available tradeline data, and may be calculated according tothe following formula:Pay-down %=(The sum of the last three months payments from theaccount)/(The sum of three month balances for the account based ontradeline data).The paydown percentage may be set to, for example, 2%, for any consumerexhibiting less than a 5% paydown percentage, and may be set to 100% ifgreater than 80%, as a simplified manner for estimating consumerspending behaviors on either end of the paydown percentage scale.

Consumers that exhibit less than a 50% paydown during this period may becategorized as revolvers, while consumers that exhibit a 50% paydown orgreater may be categorized as transactors. These categorizations may beused to initially determine what, if any, purchasing incentives may beavailable to the consumer, as described later below.

The process 600, then continues to step 606, where balance transfers fora previous period of time are identified from the available tradelinedata for the consumer. The identification of balance transfers areessential since, although tradeline data may reflect a higher balance ona credit account over time, such higher balance may simply be the resultof a transfer of a balance into the account, and are thus not indicativeof a true increase in the consumer's spending. It is difficult toconfirm balance transfers based on tradeline data since the informationavailable is not provided on a transaction level basis. In addition,there are typically lags or absences of reporting of such values ontradeline reports.

Nonetheless, marketplace analysis using confirmed consumer panel andinternal customer financial records has revealed reliable ways in whichbalance transfers into an account may be identified from imperfectindividual tradeline data alone. Three exemplary reliable methods foridentifying balance transfers from credit accounts, each which is basedin part on actual consumer data sampled, are as follows. It should bereadily apparent that these formulas in this form are not necessary forall embodiments of the present process and may vary based on theconsumer data used to derive them.

A first rule identifies a balance transfer for a given consumer's creditaccount as follows. The month having the largest balance increase in thetradeline data, and which satisfies the following conditions, may beidentified as a month in which a balance transfer has occurred:

-   -   The maximum balance increase is greater than twenty times the        second maximum balance increase for the remaining months of        available data;    -   The estimated pay-down percent calculated at step 306 above is        less than 40%; and    -   The largest balance increase is greater than $1000 based on the        available data.

A second rule identifies a balance transfer for a given consumer'scredit account in any month where the balance is above twelve times theprevious month's balance and the next month's balance differs by no morethan 20%.

A third rule identifies a balance transfer for a given consumer's creditaccount in any month where:

-   -   the current balance is greater than 1.5 times the previous        month's balance;    -   the current balance minus the previous month's balance is        greater than $4500; and    -   the estimated paydown percent from step 306 above is less than        30%.

The process 600 then continues to step 608, where consumer spend on eachcredit account is estimated over the next, for example, three monthperiod. In estimating consumer spend, any spending for a month in whicha balance transfer has been identified from individual tradeline dataabove is set to zero for purposes of estimating the size of theconsumer's spending wallet, reflecting the supposition that no realspending has occurred on that account. The estimated spend for each ofthe three previous months may then be calculated as follows:Estimated spend=(the current balance−the previous month's balance+(theprevious month's balance*the estimated pay-down % from step 604 above).The exact form of the formula selected may be based on the category inwhich the consumer is identified from the model applied, and the formulais then computed iteratively for each of the three months of the firstperiod of consumer spend.

Next, at step 610 of the process 600, the estimated spend is thenextended over, for example, the previous three quarterly or three-monthperiods, providing a most-recent year of estimated spend for theconsumer.

Finally, at step 612, this in turn may be used to generate a pluralityof final outputs for each consumer account (step 314). These may beprovided in an output file that may include a portion or all of thefollowing exemplary information, based on the calculations above andinformation available from individual tradeline data: (i) size ofprevious twelve month spending wallet; (ii) size of spending wallet foreach of the last four quarters; (iii) total number of revolving cards,revolving balance, and average pay down percentage for each; (iv) totalnumber of transacting cards, and transacting balances for each; (v) thenumber of balance transfers and total estimated amount thereof; (vi)maximum revolving balance amounts and associated credit limits; and(vii) maximum transacting balance and associated credit limit.

After step 612, the process 600 ends with respect to the examinedconsumer. It should be readily appreciated that the process 600 may berepeated for any number of current customers or consumer prospects.

Referring now to FIGS. 7-10, therein is depicted illustrative diagrams700-1000 of how such estimated spending is calculated in a rollingmanner across each previous three month (quarterly) period. In FIG. 7,there is depicted a first three month period (i.e., the most recentprevious quarter) 702 on a timeline 710. As well, there is depicted afirst twelve-month period 704 on a timeline 708 representing the lasttwenty-one months of point-in-time account balance information availablefrom individual tradeline data for the consumer's account. Each month'sbalance for the account is designated as “B#.” B1-B12 represent actualaccount balance information available over the past twelve months forthe consumer. B13-B21 represent consumer balances over consecutive,preceding months.

In accordance with the diagram 700, spending in each of the three monthsof the first quarter 702 is calculated based on the balance valuesB1-B12, the category of the consumer based on consumer spending modelsgenerated in the process 200, and the formulas used in steps 604 and606.

Turning now to FIG. 8, there is shown a diagram 800 illustrating thebalance information used for estimating spending in a second previousquarter 802 using a second twelve-month period of balance information804. Spending in each of these three months of the second previousquarter 802 is based on known balance information B4-B15.

Turning now to FIG. 9, there is shown a diagram 900 illustrating thebalance information used for estimating spending in a third successivequarter 902 using a third twelve-month period of balance information904. Spending in each of these three months of the third previousquarter 902 is based on known balance information B7-B18.

Turning now to FIG. 10, there is shown a diagram 1000 illustrating thebalance information used for estimating spending in a fourth previousquarter 1002 using a fourth twelve-month period of balance information1004. Spending in each of these three months of the fourth previousquarter 1002 is based on balance information B10-B21.

It should be readily appreciated that as the rolling calculationsproceed, the consumer's category may change based on the outputs thatresult, and, therefore, different formula corresponding to the newcategory may be applied to the consumer for different periods of time.The rolling manner described above maximizes the known data used forestimating consumer spend in a previous twelve month period 1006.

Based on the final output generated for the customer, commensuratepurchasing incentives may be identified and provided to the consumer,for example, in anticipation of an increase in the consumer's purchasingability as projected by the output file. In such cases, consumers ofgood standing, who are categorized as transactors with a protectedincrease in purchasing ability, may be offered a lower financing rate onpurchases made during the period of expected increase in theirpurchasing ability, or may be offered a discount or rebate fortransactions with selected merchants during that time.

In another example, and in the case where a consumer is a revolver, suchconsumer with a projected increase in purchasing ability may be offereda lower annual percentage rate on balances maintained on their creditaccount.

Other like promotions and enhancements to consumers' experiences arewell known and may be used within the processes disclosed herein.

Various statistics for the accuracy of the processes 200 and 600 areprovided in FIGS. 11-18, for which a consumer sample was analyzed by theprocess 200 and validated using 24 months of historic actual spend data.The table 1100 of FIG. 11 shows the number of consumers having a balanceof $5000 or more for whom the estimated paydown percentage (calculatedin step 604 above) matched the actual paydown percentage (as determinedfrom internal transaction data and external consumer panel data).

The table 1200 of FIG. 12 shows the number of consumers having a balanceof $5000 or more who were expected to be transactors or revolvers, andwho actually turned out to be transactors and revolvers based on actualspend data. As can be seen, the number of expected revolvers who turnedout to be actual revolvers (80539) was many tithes greater than thenumber of expected revolvers who turned out to be transactors (1090).Likewise, the number of expected and actual transactors outnumbered bynearly four-to-one the number of expected transactors that turned out tobe revolvers.

The table 1300 of FIG. 13 shows the number of estimated versus actualinstances in the consumer sample of when there occurred a balancetransfer into an account. For instance, in the period sampled, therewere 148,326 instances where no balance transfers were identified instep 606 above, and for which a comparison of actual consumer datashowed there were in fact no balance transfers in. This compares to only9,534 instances where no balance transfers were identified in step 606,but there were in fact actual balance transfers.

The table 1400 of FIG. 14 shows the accuracy of estimated spending (insteps 608-612) versus actual spending for consumers with accountbalances (at the time this sample testing was performed) greater than$5000. As can be seen, the estimated spending at each spending levelmost closely matched the same actual spending level than for any otherspending level in nearly all instances.

The table 1500 of FIG. 15 shows the accuracy of estimated spending (insteps 608-612) versus actual spending for consumers having most recentaccount balances between $1600 and $5000. As can be readily seen, theestimated spending at each spending level most closely matched the sameactual spending level than for any other spending level in allinstances.

The table 1600 of FIG. 16 shows the accuracy of estimated spendingversus actual spending for all consumers in the sample. As can bereadily seen, the estimated spending at each spending level most closelymatched the same actual spending level than for any other actualspending level in all instances.

The table 1700 of FIG. 17 shows the rank order of estimated versusactual spending for all consumers in the sample. This table 1700 readilyshows that the number of consumers expected to be in the bottom 10% ofspending most closely matched the actual number of consumers in thatcategory, by 827,716 to 22,721. The table 1700 further shows that thenumber of consumers expected to be in the top 10% of spenders mostclosely matched the number of consumers who were actually in the top10%, by 71,773 to 22,721.

The table 1800 of FIG. 18 shows estimated versus actual annual spendingfor all consumers in the sample over the most recent year of availabledata. As can be readily seen, the expected number of consumers at eachspending level most closely matched the same actual spending level thanany other level in all instances.

Finally, the table 1900 of FIG. 19 shows the rank order of estimatedversus actual total annual spending for all the consumers over the mostrecent year of available data. Again, the number of expected consumersin each rank most closely matched the actual rank than any other rank.

Prospective customer populations used for modeling and/or laterevaluation may be provided from any of a plurality of availablemarketing groups, or may be culled from credit bureau data, targetedadvertising campaigns or the like. Testing and analysis may becontinuously performed to identify the optimal placement and requiredfrequency of such sources for using the size of spending walletcalculations. The processes described herein may also be used to developmodels for predicting a size of wallet for an individual consumer.

Institutions adopting the processes disclosed herein may expect to morereadily and profitably identify opportunities for prospect and customerofferings, which in turn provides enhanced experiences across all partsof a customer's lifecycle. In the case of a credit provider, accurateidentification of spend opportunities allows for rapid provisioning ofcard member offerings to increase spend that, in turn, results inincreased transaction fees, interest charges and the like. The carefulselection of customers to receive such offerings reduces the incidenceof fraud that may occur in less disciplined card member incentiveprograms. This, in turn, reduces overall operating expenses forinstitutions.

III. Household Size of Wallet

In addition to determining the size of wallet of a single consumer, theabove process may also be used in determining the size of wallet of agiven household. Determining the size of wallet of a household allows afinancial institution to more accurately estimate the spend opportunityassociated with an individual than would be estimated from theindividual's size of wallet alone. For example, two example consumersmay have the same individual size of wallet. However, one consumer issingle and lives alone, but the other consumer is married to a spousewhose size of wallet is twice as big as the second consumer. The secondconsumer thus has more spending potential than the first, even thoughthey look very similar when standing alone.

FIG. 20 is a flowchart of an exemplary method 2000 of determining thesize of wallet of an entire household. In step 2002, individualconsumers are grouped into households. A household may include, forexample, all people with credit bureau history that live at the sameaddress. Such individuals do not necessarily need to have the same lastname. The grouping may exclude certain people, such as those under theage of 18, or those who have opted out of direct marketing campaigns.

In step 2004, once individual consumers are grouped into a household,tradelines held by one or more of the consumers in the household areidentified and associated with the household. The tradelines may bedetermined using, for example and without limitation, credit bureau dataand internal records of the financial institution.

In step 2006, duplicate tradelines are identified and removed fromassociation with the household, such that only unique trades remainassociated with the household. Duplicates occur when the basic user ofan account shares a household with a supplemental user of the sameaccount. To identify duplicate tradelines, the history is obtained forevery tradeline associated with the household. The history may belimited to a given timeframe, such as the previous 24 months. Thishistory may include, for example and without limitation, account balanceand transaction information. The histories of the tradelines are thencompared to determine if any tradeline in the household has the samehistorical performance as another tradeline in the household. If twotradelines are identified as having the same historical performance, oneof the tradelines is determined to be a duplicate, and is not consideredin the household size of wallet calculation.

In step 2008, an estimated spend capacity for each of the remaining,unique tradelines is calculated based on the balance of the tradeline.The estimated spend capacity may be calculated, for example, asdescribed with respect to method 600 (FIG. 6) above.

In step 2010, the estimated spend capacities of the unique tradelines inthe household are summed. The resulting combined spend capacity isoutput as the household size of wallet. The household size of wallet canthen be associated with each individual consumer in the household.

Once individual consumers are tagged with or otherwise identified bytheir household size of wallet, a financial institution can moreaccurately categorize the consumers and provide the consumers with morerelevant offers. For example, based on the household size of walletcalculated for an existing customer, purchasing incentives may beidentified and provided to the existing customer to encourage spend onan existing account. In another example, prospective customers may betargeted based on their own specific household sizes of wallet and/orspend characteristics of other consumers in their household. In thisexample, a prospective cardholder whose household size of wallet issignificantly higher than his individual size of wallet is expected tohave high spend and a high response rate to product offers. Similarly, aprospective cardholder that lives in the same household as a high spend,low risk card holder is expected to be high spend and low risk as well.Such targeting encourages spend by prospective cardholders on newaccounts.

Categorizing consumers by household type reveals trends which can beused to identify low risk prospects without completing size of walletanalyses for each specific prospect. The household size and mix ofconsumers therein defines a household type. FIG. 21 is a chartidentifying various household types 2102. Each household type 2102 has aparticular size 2104 and a particular mix 2106. Size 2104 corresponds tothe number of consumers in the household. Mix 2106 corresponds to thenumber of basic cardholders, supplemental cardholders, and prospectivecardholders in the household. Each household type makes up a percentage2108 of all households.

FIG. 22 illustrates average sizes of wallet by household type. As wouldbe expected, household size of wallet increases as the number of peoplein a household increases, and depends on the mix of consumers in thehousehold. Of households with two people, for example, those having twobasic cardholders (type 2A) tend to have the largest wallet, whilehouseholds having one basic cardholder and one prospective cardholder(type 2C) tend to have the smallest wallet. In another example, ofhouseholds with three people, those having two basic cardholders and oneprospective cardholder (type 3C) tend to have the largest wallets(excluding the “other” category), while households having one basiccardholder and two prospective cardholders (type 3A) tend to have thesmallest wallets.

By identifying the types of households having the largest wallets, afinancial institution can target consumers in those household types withnew product offers and/or incentives on existing products to encouragespend with the financial institution by the consumers. For example, thefinancial institution can target prospective cardholders of all type-2Ahouseholds with an offer for a new card product that suits their needs,since those cardholders are the most likely to accept such an offerwhile maintaining a low risk of default.

Once the size of wallet has been determined for a given household, theshare of the household wallet held by a particular financial institutioncan also be determined. The share of wallet is the percentage of thetotal size of wallet that is associated with the financial institutionand can typically be determined, for example, from the internal recordsof the financial institution. By identifying households where thefinancial institution has only a small share of the household size ofwallet, the financial institution can determine which households offerthe best prospects for spending growth. This is referred to as the spendopportunity. Households having a large spend opportunity can then betargeted for product offers and incentives to increase spend by theconsumer with the financial institution. For example, for a financialinstitution having the exemplary share of household wallet distributionillustrated in FIG. 23, the greatest spend opportunity is available inhouseholds having one basic cardholder and one supplemental cardholder(type 2B) and households having one basic cardholder and one prospectivecardholder (type 2C).

IV. Conclusion

While various embodiments of the present invention have been describedabove, it should be understood that they have been presented by way ofexample, and not limitation. It will be apparent to persons skilled inthe relevant art(s) that various changes in form and detail can be madetherein without departing from the spirit and scope of the presentinvention. Thus, the present invention should not be limited by any ofthe above described exemplary embodiments, but should be defined only inaccordance with the following claims and their equivalents.

In addition, it should be understood that the figures and screen shotsillustrated in the attachments, which highlight the functionality andadvantages of the present invention, are presented for example purposesonly. The architecture of the present invention is sufficiently flexibleand configurable, such that it may be utilized (and navigated) in waysother than that shown in the accompanying figures.

Further, the purpose of the foregoing Abstract is to enable the U.S.Patent and Trademark Office and the public generally, and especially thescientists, engineers and practitioners in the art who are not familiarwith patent or legal terms or phraseology, to determine quickly from acursory inspection the nature and essence of the technical disclosure ofthe application. The Abstract is not intended to be limiting as to thescope of the present invention in any way.

What is claimed is:
 1. A method comprising: extracting, by acomputer-based system and using a first application programminginterface (API) having continuous access, current spending data for aconsumer from a transaction processing system internet website thatprocessed the spending data for the consumer; extracting, by thecomputer-based system and using a second API having continuous access,current consumer panel data from consumer research companies' internetwebsites; extracting, by the computer-based system and using a third APIhaving continuous access, current consumer tradeline data from consumercredit companies' internet websites, wherein the extracting stepsprovide continuous access to the current spending data, the currentconsumer panel data, and the current consumer tradeline data; modeling,by the computer-based system, consumer behavior and attributes includingthe consumer panel data and the consumer tradeline data, by using timeseries tradeline data and raw tradeline data; enhancing, by thecomputer-based system, validation and refinement of an accuracy of themodeling via the continuous access to the current spending data, thecurrent consumer panel data and the current consumer tradeline data;verifying, by the computer-based system and periodically on an ongoingbasis, an applicability of the modeling to the consumer; determining, bythe computer-based system and based on the modeling, a plurality oftradelines using point in time balance information; associating, by thecomputer-based system, the plurality of tradelines with a household ofthe consumer; removing, by the computer-based system, tradelinesassociated with a supplemental consumer in the plurality of tradelinesfrom consideration in calculating a household size of wallet of theconsumer, to create a subset of tradelines; and calculating, by thecomputer-based system, the household size of wallet based on the subsetof tradelines.
 2. The method of claim 1, wherein the removing is alsobased on comparing histories of tradelines.
 3. The method of claim 1,wherein the removing is also based on comparing histories of tradelineswithin a time period.
 4. The method of claim 1, wherein the removingcomprises determining tradelines associated with the supplementalconsumer based on comparing histories of tradelines.
 5. The method ofclaim 1, wherein the tradelines associated with the supplementalconsumer comprise duplicate tradelines.
 6. The method of claim 1,wherein the removing is also based on comparing histories of tradelines,and wherein the histories of tradelines include at least one of accountbalance or transaction information.
 7. The method of claim 1, furthercomprising estimating, by the computer-based system, a spend capacityfor each tradeline within the subset of tradelines.
 8. The method ofclaim 1, wherein the calculating step comprises summing spend capacitiesfor each tradeline within the subset of tradelines.
 9. The method ofclaim 1, further comprising associating the household size of walletwith the consumer.
 10. The method of claim 1, further comprisingtargeting the consumer with a new product offer based on the householdsize of wallet.
 11. The method of claim 1, further comprising targetingthe consumer with a spend incentive for an existing product based on thehousehold size of wallet.
 12. The method of claim 1, wherein theremoving results in unique tradelines being associated with thehousehold.
 13. The method of claim 1, wherein the removing results indifferent tradelines being associated with the household.
 14. A system,comprising: a non-transitory memory communicating with a processor; thenon-transitory memory having instructions stored thereon that, inresponse to execution by the processor, cause the processor to performoperations comprising: extracting, by the computer-based system andusing a first application programming interface (API) having continuousaccess, current spending data for a consumer from a transactionprocessing system internet website that processed the spending data forthe consumer; extracting, by the computer-based system and using asecond API having continuous access, current consumer panel data fromconsumer research companies' internet websites; extracting, by thecomputer-based system and using a third API having continuous access,current consumer tradeline data from consumer credit companies' internetwebsites, wherein the extracting steps provide continuous access to thecurrent spending data, the current consumer panel data, and the currentconsumer tradeline data; modeling, by the computer-based system,consumer behavior and attributes including the consumer panel data andthe consumer tradeline data, by using time series tradeline data and rawtradeline data; enhancing, by the computer-based system, validation andrefinement of an accuracy of the modeling via the continuous access tothe current spending data, the current consumer panel data and thecurrent consumer tradeline data; verifying, by the computer-based systemand periodically on an ongoing basis, an applicability of the modelingto the consumer; determining, by the computer-based system and based onthe modeling, a plurality of tradelines using point in time balanceinformation; associating, by the computer-based system, the plurality oftradelines with a household of the consumer; removing, by thecomputer-based system, tradelines associated with a supplementalconsumer in the plurality of tradelines from consideration incalculating a household size of wallet of the consumer, to create asubset of tradelines; and calculating, by the computer-based system, thehousehold size of wallet based on the subset of tradelines.
 15. Thesystem of claim 14, wherein the removing is also based on comparinghistories of tradelines.
 16. The system of claim 14, wherein theremoving is also based on comparing histories of tradelines within atime period.
 17. The system of claim 14, wherein the removing comprisesdetermining tradelines associated with the supplemental consumer basedon comparing histories of tradelines.
 18. The system of claim 14,wherein the tradelines associated with the supplemental consumercomprise duplicate tradelines.
 19. The system of claim 14, wherein theremoving is also based on comparing histories of tradelines, and whereinthe histories of tradelines include at least one of account balance ortransaction information.
 20. A non-transitory computer readable storagemedium bearing instructions, when executed by a processor, cause theprocessor to perform operations comprising: extracting, by thecomputer-based system and using a first application programminginterface (API) having continuous access, current spending data for aconsumer from a transaction processing system internet website thatprocessed the spending data for the consumer; extracting, by thecomputer-based system and using a second API having continuous access,current consumer panel data from consumer research companies' internetwebsites; extracting, by the computer-based system and using a third APIhaving continuous access, current consumer tradeline data from consumercredit companies' internet websites, wherein the extracting stepsprovide continuous access to the current spending data, the currentconsumer panel data, and the current consumer tradeline data; modeling,by the computer-based system, consumer behavior and attributes includingthe consumer panel data and the consumer tradeline data, by using timeseries tradeline data and raw tradeline data; enhancing, by thecomputer-based system, validation and refinement of an accuracy of themodeling via the continuous access to the current spending data, thecurrent consumer panel data and the current consumer tradeline data;verifying, by the computer-based system and periodically on an ongoingbasis, an applicability of the modeling to the consumer; determining, bythe computer-based system and based on the modeling, a plurality oftradelines using point in time balance information; associating, by thecomputer-based system, the plurality of tradelines with a household ofthe consumer; removing, by the computer-based system, tradelinesassociated with a supplemental consumer in the plurality of tradelinesfrom consideration in calculating a household size of wallet of theconsumer, to create a subset of tradelines; and calculating, by thecomputer-based system, the household size of wallet based on the subsetof tradelines.